AI performance evaluation: Robot hand assessing documents while a human eye oversees.

AI in Management: Can Algorithms Replace Human Judgment?

Mira Elwood in Tech & Innovation December 2025 • 4 min read.

"Exploring the Potential of Large Language Models in Performance Evaluation"

In today's fast-paced business environment, organizations are constantly seeking ways to improve efficiency and objectivity in performance evaluations. Traditional methods often rely on human judgment, which can be subjective and prone to biases. But what if artificial intelligence could offer a more reliable and consistent approach?

A groundbreaking new study published explores the potential of Large Language Models (LLMs), specifically GPT-4, to revolutionize performance evaluations in management. This research investigates whether AI algorithms can accurately and fairly assess employee performance, and how they compare to human raters.

This article dives into the key findings of this study, revealing the surprising strengths and weaknesses of LLMs in performance evaluation. We'll explore how AI can enhance objectivity, where it falls prey to biases, and what this means for the future of work and human resources.

The Rise of AI Raters: How LLMs Evaluate Performance

AI performance evaluation: Robot hand assessing documents while a human eye oversees.

The study's core premise centers around the idea that LLMs can analyze text-based data – such as reports, memos, and strategic plans – to evaluate employee performance. Unlike traditional Natural Language Processing (NLP) techniques, LLMs like GPT-4 possess zero-shot learning capabilities, meaning they can assess tasks without prior training. This is a game-changer, as it eliminates the need for extensive pre-labeled data and allows for rapid deployment.

To test this premise, the researchers conducted two comprehensive studies:

Study 1: Participants completed professional tasks in a controlled laboratory setting, and their outputs were evaluated by both human raters and LLMs.
Study 2: Real-world performance evaluations from a Chinese taxi company were analyzed, with LLMs and human raters assessing employee outputs. This study also investigated the impact of bias by manipulating background information about employees.

Across both studies, the researchers compared LLM ratings to human ratings, focusing on accuracy, consistency, and susceptibility to biases.

The Future of AI in Management: A Balanced Perspective

The study on LLMs is a crucial step towards understanding the potential and limitations of AI in management. While AI offers unprecedented opportunities for efficiency and objectivity, it's essential to acknowledge and address the biases that can creep into algorithms. By combining the strengths of AI with human oversight, organizations can create more accurate, fair, and effective performance evaluation systems, driving both employee growth and organizational success.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2408.05328,

Title: From Text To Insight: Leveraging Large Language Models For Performance Evaluation In Management

Subject: cs.cl cs.ai cs.et cs.hc econ.gn q-fin.ec

Authors: Ning Li, Huaikang Zhou, Mingze Xu

Published: 09-08-2024

Everything You Need To Know

What are Large Language Models (LLMs) and how are they being used in performance evaluations?

Large Language Models (LLMs), such as GPT-4, are advanced AI systems capable of analyzing text-based data to assess employee performance. Unlike traditional Natural Language Processing (NLP) methods, LLMs utilize zero-shot learning, enabling them to evaluate tasks without prior training. This involves analyzing reports, memos, and strategic plans, offering a potential for automated and objective performance reviews within organizations.

How does the use of AI, specifically LLMs like GPT-4, compare to traditional human raters in evaluating employee performance?

The research compared the accuracy, consistency, and bias susceptibility of LLMs to those of human raters. The studies involved evaluating employee performance in both controlled laboratory settings and real-world scenarios, such as a Chinese taxi company. The findings reveal the strengths and weaknesses of LLMs in comparison to human raters, highlighting the potential for enhanced objectivity but also the risk of biases. This comparison aims to understand the role of AI in creating effective performance evaluation systems.

What are the practical implications of using LLMs, like GPT-4, for evaluating employees in the workplace?

The adoption of LLMs in performance evaluations introduces both opportunities and challenges. On the one hand, LLMs offer the potential for increased efficiency and objectivity in the evaluation process. On the other hand, it is essential to acknowledge and address the biases that can be embedded within algorithms. Organizations can leverage AI's strengths and human oversight to create fairer, more accurate, and more effective performance evaluation systems.

Can you explain the methodology used in the studies that assessed Large Language Models (LLMs) in performance evaluations?

The studies used two main approaches. Study 1 involved participants completing professional tasks in a controlled laboratory environment, where both human raters and LLMs evaluated their outputs. Study 2 analyzed real-world performance evaluations from a Chinese taxi company, with both LLMs and human raters assessing employee outputs. This study also investigated the impact of bias by manipulating background information about employees. The comparison focused on accuracy, consistency, and susceptibility to biases to understand the effectiveness of LLMs.

What are the key benefits and drawbacks of using AI in performance evaluations, and how can organizations navigate these challenges?

The key benefits of using AI, particularly Large Language Models (LLMs), include the potential for increased efficiency and objectivity in performance evaluations. A significant drawback, however, is the susceptibility of algorithms to biases. Organizations can navigate these challenges by combining AI's strengths with human oversight. This approach allows for the creation of more accurate, fair, and effective performance evaluation systems, promoting both employee growth and organizational success. It's essential to be aware of and actively manage the potential biases that AI might introduce.